Industrial processes with large, unknown and/or time varying process delays are difficult to control. Self-tuning, in its present form, is typically inapplicable to this class of processes due to their reliance on recursive least squares parameter estimation and the PID control algorithm. Recursive least squares parameter estimation techniques typically fail because of a requirement for explicit knowledge of the process deadtime. Similarly, control systems utilizing a PID controller are unable to directly compensate for process deadtime. To maintain loop stability, these control systems must be detuned, degrading overall controller performance.
One approach for extending self-tuning control to the foregoing class of processes is to join a recursive least squares parameter estimation algorithm with a deadtime estimator and augment the control function with deadtime compensation. Two particular methods, one employing a bank of estimators with different assumed values for the deadtime, and the second employing a single estimator to identify a set of parameters extended over a time period so as to include an assumed deadtime, have received significant attention. Unfortunately, these techniques have proven impractical for general purpose application in an industrial setting.
Because of the foregoing, it has become desirable to develop a self-tuning control technique that incorporates both a parameter estimation algorithm and an algorithm for deadtime estimation.